Secure IoT edge: Threat situation awareness based on network traffic

Zhao, Yuyu; Cheng, Guang; Duan, Yu; Gu, Zhouchao; Zhou, Yuyang; Tang, Lu

doi:10.1016/j.comnet.2021.108525

Cited by 13 publications

(7 citation statements)

References 45 publications

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“…We further evaluated the prediction performance of dynamic defense and compared it with TSA-AdaBoost [ 49 ], a situation awareness algorithm based on the AdaBoost machine learning method. DDESS adopts the batch normalization [ 40 ] to reduce the influence of gradient vanishing problem, and max-pooling layer and

convolutional kernel [ 41 ] for dimensionality reduction.…”

Section: Methodsmentioning

confidence: 99%

Towards Double Defense Network Security Based on Multi-Identifier Network Architecture

Wang

Smahi

Zhang

et al. 2022

Sensors

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Recently, more and more mobile devices have been connected to the Internet. The Internet environment is complicated, and network security incidents emerge endlessly. Traditional blocking and killing passive defense measures cannot fundamentally meet the network security requirements. Inspired by the heuristic establishment of multiple lines of defense in immunology, we designed and prototyped a Double Defense strategy with Endogenous Safety and Security (DDESS) based on multi-identifier network (MIN) architecture. DDESS adopts the idea of a zero-trust network, with identity authentication as the core for access control, which solves security problems of traditional IP networks. In addition, DDESS achieves individual static security defense through encryption and decryption, consortium blockchain, trusted computing whitelist, and remote attestation strategies. At the same time, with the dynamic collection of data traffic and access logs, as well as the understanding and prediction of the situation, DDESS can realize the situation awareness of network security and the cultivation of immune vaccines against unknown network attacks, thus achieving the active herd defense of network security.

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convolutional kernel [ 41 ] for dimensionality reduction.…”

Section: Methodsmentioning

confidence: 99%

Towards Double Defense Network Security Based on Multi-Identifier Network Architecture

Wang

Smahi

Zhang

et al. 2022

Sensors

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“…Therefore, we can use an FPGA to configure a circuit and then change the configured circuit to support another one. Based upon this logic, we integrate an FPGA into an IoT circuit or interface an FPGA with an IoT circuit [102]. However, the integrated FPGA has some threat-aware components [103], which would become activated when the IoT network endures a DDoS attack and note down the attackers' IP addresses.…”

Section: Ddos Detection and Mitigation: The Iot-fpga Approachmentioning

confidence: 99%

A Survey of DDOS Attack Detection Techniques for IoT Systems Using BlockChain Technology

Khan

Namin

2022

Electronics

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The Internet of Things (IoT) is a network of sensors that helps collect data 24/7 without human intervention. However, the network may suffer from problems such as the low battery, heterogeneity, and connectivity issues due to the lack of standards. Even though these problems can cause several performance hiccups, security issues need immediate attention because hackers access vital personal and financial information and then misuse it. These security issues can allow hackers to hijack IoT devices and then use them to establish a Botnet to launch a Distributed Denial of Service (DDoS) attack. Blockchain technology can provide security to IoT devices by providing secure authentication using public keys. Similarly, Smart Contracts (SCs) can improve the performance of the IoT–blockchain network through automation. However, surveyed work shows that the blockchain and SCs do not provide foolproof security; sometimes, attackers defeat these security mechanisms and initiate DDoS attacks. Thus, developers and security software engineers must be aware of different techniques to detect DDoS attacks. In this survey paper, we highlight different techniques to detect DDoS attacks. The novelty of our work is to classify the DDoS detection techniques according to blockchain technology. As a result, researchers can enhance their systems by using blockchain-based support for detecting threats. In addition, we provide general information about the studied systems and their workings. However, we cannot neglect the recent surveys. To that end, we compare the state-of-the-art DDoS surveys based on their data collection techniques and the discussed DDoS attacks on the IoT subsystems. The study of different IoT subsystems tells us that DDoS attacks also impact other computing systems, such as SCs, networking devices, and power grids. Hence, our work briefly describes DDoS attacks and their impacts on the above subsystems and IoT. For instance, due to DDoS attacks, the targeted computing systems suffer delays which cause tremendous financial and utility losses to the subscribers. Hence, we discuss the impacts of DDoS attacks in the context of associated systems. Finally, we discuss Machine-Learning algorithms, performance metrics, and the underlying technology of IoT systems so that the readers can grasp the detection techniques and the attack vectors. Moreover, associated systems such as Software-Defined Networking (SDN) and Field-Programmable Gate Arrays (FPGA) are a source of good security enhancement for IoT Networks. Thus, we include a detailed discussion of future development encompassing all major IoT subsystems.

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“…However, as the number of devices upsurges, so does the risk of cyber threats, particularly botnets. Botnets are collections of compromised devices that a cybercriminal can remotely operate without the owner's knowledge or permission [4]. Botnets can be used to carry out a range of malicious activities, including Distributed Denial of Service attacks, spamming, phishing, and crypto currency mining.…”

Section: Introductionmentioning

confidence: 99%

“…Different Deep Learning (DL) techniques have been suggested in earlier studies as a means of defending communication networks from cyber-attacks. It might be difficult to offload enormous distributed IoT network traffic data to a remote central cloud server for real-time data processing, nevertheless, as IoT networks continue to grow and become more scalable [4]. The CDL technique also needs more time for training, has a lot of communication overhead, and takes up a lot of RAM for data storage [1].…”

Section: Introductionmentioning

confidence: 99%

“…Lack of prior knowledge regarding the frequency of such attacks is the primary cause of the difficulties in identifying zero-day attacks [1]. The capacity to identify zero-day botnet assaults with a high detection rate and a low false alarm rate is thus necessary for creating an intrusion detection system for IoT edge networks [4]. The BAI approach is what we recommend for identifying zero-day botnet assaults in IoT edge devices.…”

Section: Introductionmentioning

confidence: 99%

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Botnet Threat Intelligence in IoT-Edge

2023

IJATCSE

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Recently, deep learning has gotten progressively popular in the domain of security. However, Traditional machine learning models are not capable to discover zero-day botnet attacks with extraordinary privacy. For this purpose, researchers have utilized deep learning based computational framework for Botnet which can detect zero-day attacks, achieve data privacy and improve training time using machine learning techniques for the IoT-edge devices. However, it combines and integrates various models and contexts. As a result, the objective of this research was to incorporate the deep learning model which controls different operation of IoT devices and reduce the training time. In deep learning, there are numerous components that aspect the false positive rate of every detected attack type. These elements are F1 score, false-positive rate, and training time; reduce the time of detection, and Accuracy. Bashlite and Mirai are two examples of zero-day botnet attacks that pose a threat to IoT edge devices. The majority of cyber-attacks are executed by malware-infected devices that are remotely controlled by attackers. This malware is often referred to as a bot or botnet, and it enables attackers to control the device and perform malicious actions, such as spamming, stealing sensitive information, and launching DDoS attacks. The model was formulated in Python libraries and subsequently tested on real life data to assess whether the integrated model performs better than its counterparts. The outcomes show that the proposed model performs in a way that is better than existing models i.e. DDL, CDL and LDL as Botnet Attacks Intelligence (BAI) the purposed deep learning model.

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Secure IoT edge: Threat situation awareness based on network traffic

Cited by 13 publications

References 45 publications

Towards Double Defense Network Security Based on Multi-Identifier Network Architecture

Towards Double Defense Network Security Based on Multi-Identifier Network Architecture

A Survey of DDOS Attack Detection Techniques for IoT Systems Using BlockChain Technology

Botnet Threat Intelligence in IoT-Edge

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